How many checkerboard photos does OpenCV camera calibration need before the focal length stops moving?

What it found

Calibrates a camera with cv2.calibrateCamera on random subsets of the 13 real 640×480 checkerboard photos shipped with OpenCV (Apache-2.0), against the same model's all-view fit. With the default five-term model the 5–95% band of the focal length is 2.3% wide at 3 views and 0.49% at 12, and 99–100% of draws are within ±2%. Re-rendering the same board poses with Gaussian noise at the measured 0.41 px reprojection error and a known truth gives 3.0% and 0.8%, so sampling noise alone predicts the spread. A lean model (one radial term, centred principal point) has a 4.4% band on real photos against 2.0% on matching synthetic ones, and 0.43 px held-out reprojection error against 0.21 px for the full model at 12 views. Limits: one camera, one tutorial board with strongly tilted views; the reference is the all-view fit, not a lens ground truth; the 12-view row overlaps heavily (13 distinct subsets); synthetic noise is isotropic Gaussian.

A figure from its own output

Paper, code and results

• README and method
• Experiments and results
• Code
• Tests

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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.